Testing Kernel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7daf6fdb",
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "attempted relative import with no known parent package",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/xf/8808rrps3wb3r170fzcq_6t00000gn/T/ipykernel_7764/3803529404.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mstatistics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstandard_deviation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mgradient_descent\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgradient_step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mprobability\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0minverse_normal_cdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/DS_002/gradient_descent.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;31m# local code imports\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mlinear_algebra\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdistance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscalar_multiply\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mlinear_algebra\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mlinear_algebra\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mvector_mean\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: attempted relative import with no known parent package"
     ]
    }
   ],
   "source": [
    "from linear_algebra import Vector, Matrix\n",
    "from linear_algebra import make_matrix\n",
    "from linear_algebra import sum_of_squares\n",
    "from linear_algebra import dot\n",
    "from linear_algebra import subtract\n",
    "from linear_algebra import magnitude\n",
    "from linear_algebra import scalar_multiply\n",
    "from linear_algebra import vector_mean\n",
    "from linear_algebra import distance\n",
    "from linear_algebra import add\n",
    "\n",
    "from statistics import correlation\n",
    "from statistics import standard_deviation\n",
    "from statistics import median\n",
    "from statistics import mean\n",
    "from statistics import de_mean\n",
    "from statistics import standard_deviation\n",
    "\n",
    "from gradient_descent import gradient_step\n",
    "\n",
    "from probability import inverse_normal_cdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "98e18445",
   "metadata": {},
   "outputs": [],
   "source": [
    "# python library imports\n",
    "import random, datetime, re, csv, math, enum\n",
    "from collections import defaultdict, Counter\n",
    "from typing import Tuple, List, NamedTuple, Optional, Callable\n",
    "from typing import TypeVar, List, Iterator\n",
    "\n",
    "# external code\n",
    "from dateutil.parser import parse\n",
    "import tqdm\n",
    "\n",
    "# pyplot configs\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# font\n",
    "plt.rcParams.update({'font.size': 8})\n",
    "\n",
    "# reset the default figsize value\n",
    "plt.rcParams[\"figure.figsize\"] = plt.rcParamsDefault[\"figure.figsize\"]\n",
    "\n",
    "# 144 is good for a high-resolution display. Try 100 if it's too big\n",
    "plt.rcParams[\"figure.dpi\"] = (80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "316a1b3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| i | col |\n",
      "|-- |---- |\n",
      "| 0 |  |\n",
      "| 1 | age |\n",
      "| 2 | workclass |\n",
      "| 3 | fnlwgt |\n",
      "| 4 | education |\n",
      "| 5 | educational-num |\n",
      "| 6 | marital-status |\n",
      "| 7 | occupation |\n",
      "| 8 | relationship |\n",
      "| 9 | race |\n",
      "| 10 | gender |\n",
      "| 11 | capital-gain |\n",
      "| 12 | capital-loss |\n",
      "| 13 | hours-per-week |\n",
      "| 14 | native-country |\n",
      "| 15 | income |\n"
     ]
    }
   ],
   "source": [
    "!head -1 /Users/nickcrites/Desktop/DS_002/adult_9ksample.csv > cols.txt; \n",
    "with open(\"cols.txt\") as f:\n",
    "    for line in f:\n",
    "        cols = line.strip().split(',')\n",
    "\n",
    "print(\"| i | col |\")\n",
    "print(\"|-- |---- |\")\n",
    "for i,c in enumerate(cols):\n",
    "  print(f\"| {i} | {c} |\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e54ff888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9001"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = \"/Users/nickcrites/Desktop/DS_002/adult_9ksample.csv\"\n",
    "with open(filename) as f:\n",
    "    numlines = sum(1 for line in f)\n",
    "\n",
    "numlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a22edb1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load up a large data structure\n",
    "data_dict = csv.DictReader(open(filename))\n",
    "\n",
    "# Let's do some frequency counting\n",
    "from collections import Counter\n",
    "\n",
    "# race, gender, occupation, hours worked per week, and education\n",
    "races = []\n",
    "genders = []\n",
    "incomes = []\n",
    "educationalnum = []\n",
    "occupations = []\n",
    "hoursperweek = []\n",
    "\n",
    "for row in data_dict:\n",
    "    hours = int(row[\"hours-per-week\"])\n",
    "    hoursperweek.append(hours)\n",
    "\n",
    "    race = str(row[\"race\"])\n",
    "    races.append(race)\n",
    "\n",
    "    gender = str(row[\"gender\"])\n",
    "    genders.append(gender)\n",
    "\n",
    "    income = str(row[\"income\"])\n",
    "    incomes.append(income)\n",
    "\n",
    "    occupation = str(row[\"occupation\"])\n",
    "    occupations.append(occupation)\n",
    "\n",
    "assert len(hoursperweek) == len(races) == len(genders) == len(incomes) == len(occupations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6aaff997",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean hours-per-week is 40.99366666666667, median is 40.0\n"
     ]
    }
   ],
   "source": [
    "print(f\"The mean hours-per-week is {mean(hoursperweek)}, median is {median(hoursperweek)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "47f22c33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 512x384 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How many unique occupations?\n",
    "o = Counter(occupations)\n",
    "# occupations\n",
    "\n",
    "sns.barplot(\n",
    "    y=list(o.keys()),\n",
    "    x=list(o.values()),\n",
    "    orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cc602039",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 512x384 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution of incomes?\n",
    "i = Counter(incomes)\n",
    "\n",
    "sns.barplot(\n",
    "    x=list(i.keys()),\n",
    "    y=list(i.values()),\n",
    "    orient='v')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f8a93edd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 512x384 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of race?\n",
    "r = Counter(races)\n",
    "\n",
    "sns.barplot(\n",
    "    x=list(r.keys()),\n",
    "    y=list(r.values()),\n",
    "    orient='v')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "caa1723a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 512x384 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution of gender?\n",
    "g = Counter(genders)\n",
    "\n",
    "sns.barplot(\n",
    "    x=list(g.keys()),\n",
    "    y=list(g.values()),\n",
    "    orient='v')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa1e76bb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}